Comprehending how neuronal networks compute is a central goal in neuroscience, but it is challenging to directly measure how information flows through and is processed by large circuits of interconnected neurons. Ideally, one would capture what every neuron represents and determine which of its counterparts this information was shared with. However, measuring neuronal activity requires high temporal resolution and finding the connections between neurons requires high spatial resolution. The constraints imposed by current techniques for evaluating neuronal population activity and network anatomy put these requirements at odds: those that sample rapidly typically do so with lower spatial resolution, while those that provide high spatial resolution generally sample slowly. Finding ways to combine the strengths of different approaches and applying them to relatively small nervous systems holds great potential for examining neuronal network function.The translucence, genetic toolset, and small size of the larval zebrafish model organism make it ideal for whole-brain activity mapping at cellular resolution while presenting sensory stimuli and recording behavior. Constant improvements to reporters of neuronal activity and light microscope designs are being made to capture snapshots of neuronal activity more rapidly. However, existing methods for identifying neuronal connectivity in larval zebrafish are applicable to only a small fraction of the population at once. An efficient way to determine the neuronal network anatomy—or wiring diagram—of a circuit is to reconstruct connections from micrographs of continuous series of thin sections acquired with electron microscopy, but this technique has yet to be applied to studying neuronal circuits in larval zebrafish. Furthermore, its use has not yet approached the scale of the complete larval zebrafish brain.This dissertation describes new tools for enhancing larval zebrafish activity mapping endeavors and the development of a serial-section electron microscopy approach to accomplish dense structural imaging of the complete brain. Together, these developments provide a foundation for studying neuronal network computation in the context of a behaving animal.